class_docstr: Softplus function that ensures a strictly positive activation
  
  Parameters
  ----------
  margin : float
      the minimum value of activation. when =0, same as vanilla softplus
  beta: float
  
  threshold: float
class_docstr_tok: ["Softplus","function","that","ensures","a","strictly","positive","activation","Parameters","--","--","--","--","--","margin",":","float","the","minimum","value","of","activation",".","when","=0",",","same","as","vanilla","softplus","beta",":","float","threshold",":","float"]
code: def forward(self, x: Tensor) -> Tensor:
      return (
          super(PositiveSoftplus, self).forward(x - self.margin)
          + self.margin
      )
  
file_path: gluonts/src/gluonts/nursery/daf/tslib/nn/activations.py
imports: from typing import List
  import math
  import torch
  from torch import Tensor, nn
label_desc: Use image node_id 2 for calling the PositiveSoftplus obj's underlying member method code with example usage: obj.forward(x) and returns: unknown